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README.md

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-license: mit
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+# Android-Projekt: ID Card Classification & Embedding Models
+
+[![License](https://img.shields.io/badge/license-MIT-blue.svg)](LICENSE)
+[![TensorFlow](https://img.shields.io/badge/TensorFlow-2.x-orange.svg)](https://www.tensorflow.org/)
+[![Platform](https://img.shields.io/badge/platform-Android-green.svg)](https://developer.android.com/)
+
+This repository contains machine learning models for ID card detection, classification, and embedding generation, optimized for Android deployment.
+
+## 📦 Models Overview
+
+| Model File | Format | Size | Description | Use Case |
+|------------|--------|------|-------------|----------|
+| `id_classifier.tflite` | TFLite | 1.11 MB | Lightweight ID classifier | Mobile inference |
+| `id_card_embedding_model.tflite` | TFLite | 1.26 MB | Compact embedding model | Mobile feature extraction |
+| `id_card_classifier.keras` | Keras | 5.23 MB | Full Keras classifier | Training/fine-tuning |
+| `id_classifier_saved_model.h5` | H5 | 8.85 MB | H5 format classifier | Legacy compatibility |
+| `id_classifier_saved_model.keras` | Keras | 12.7 MB | Complete Keras model | Development/evaluation |
+| `id_card_embedding_model.keras` | Keras | 191 MB | High-accuracy embedding model | Server-side processing |
+
+## 🚀 Quick Start
+
+### For Android Development (TFLite)
+
+```kotlin
+// Load TFLite model in Android
+val model = Interpreter(loadModelFile("id_classifier.tflite"))
+
+// Prepare input
+val inputBuffer = ByteBuffer.allocateDirect(inputSize)
+val outputBuffer = ByteBuffer.allocateDirect(outputSize)
+
+// Run inference
+model.run(inputBuffer, outputBuffer)
+```
+
+### For Python/Training (Keras)
+
+```python
+from tensorflow.keras.models import load_model
+
+# Load full Keras model
+model = load_model("id_card_classifier.keras")
+
+# Make predictions
+predictions = model.predict(input_data)
+```
+
+### For TFLite Interpreter
+
+```python
+import tensorflow as tf
+
+# Load TFLite model
+interpreter = tf.lite.Interpreter(model_path="id_card_embedding_model.tflite")
+interpreter.allocate_tensors()
+
+# Get input and output details
+input_details = interpreter.get_input_details()
+output_details = interpreter.get_output_details()
+
+# Run inference
+interpreter.set_tensor(input_details[0]['index'], input_data)
+interpreter.invoke()
+output = interpreter.get_tensor(output_details[0]['index'])
+```
+
+## 📥 Download & Installation
+
+### Clone with Git LFS
+
+```bash
+git lfs install
+git clone https://huggingface.co/Ajay007001/Android-Projekt
+```
+
+### Download Specific Model
+
+```python
+from huggingface_hub import hf_hub_download
+
+model_path = hf_hub_download(
+    repo_id="Ajay007001/Android-Projekt",
+    filename="id_classifier.tflite"
+)
+```
+
+## 🔧 Model Details
+
+### ID Classifier
+- **Purpose**: Classify different types of ID cards
+- **Input**: Preprocessed ID card images
+- **Output**: Classification probabilities
+- **Recommended for**: Real-time mobile applications
+
+### Embedding Model
+- **Purpose**: Generate feature embeddings for ID cards
+- **Input**: Raw or preprocessed ID card images
+- **Output**: High-dimensional feature vectors
+- **Recommended for**: Similarity search, verification systems
+
+## 💡 Integration Tips
+
+### Android Studio Setup
+
+1. Place `.tflite` files in `app/src/main/assets/`
+2. Add TensorFlow Lite dependency:
+
+```gradle
+implementation 'org.tensorflow:tensorflow-lite:2.14.0'
+implementation 'org.tensorflow:tensorflow-lite-support:0.4.4'
+```
+
+3. Load model in your Activity/Fragment
+
+### Memory Considerations
+
+⚠️ **Important**: The `id_card_embedding_model.keras` (191 MB) requires significant memory. For mobile deployment, use the `.tflite` versions instead.
+
+## 📊 Performance
+
+- **TFLite models**: Optimized for mobile inference (<50ms on modern Android devices)
+- **Keras models**: Full precision for training and evaluation
+- **Recommended**: Use `.tflite` for production Android apps
+
+## 🛠️ Development
+
+### Fine-tuning
+
+```python
+# Load base model
+base_model = load_model("id_card_classifier.keras")
+
+# Freeze layers
+for layer in base_model.layers[:-5]:
+    layer.trainable = False
+
+# Add custom layers
+# ... your custom architecture
+
+# Compile and train
+model.compile(optimizer='adam', loss='categorical_crossentropy')
+model.fit(train_data, epochs=10)
+```
+
+### Convert to TFLite
+
+```python
+import tensorflow as tf
+
+# Load Keras model
+model = tf.keras.models.load_model("id_card_classifier.keras")
+
+# Convert to TFLite
+converter = tf.lite.TFLiteConverter.from_keras_model(model)
+converter.optimizations = [tf.lite.Optimize.DEFAULT]
+tflite_model = converter.convert()
+
+# Save
+with open("model.tflite", "wb") as f:
+    f.write(tflite_model)
+```
+
+## 📝 Notes
+
+- All models use Git LFS for efficient storage and download
+- TFLite models are quantized for optimal mobile performance
+- Keras models retain full precision for training purposes
+- Compatible with TensorFlow 2.x
+
+## 🤝 Contributing
+
+Contributions are welcome! Please feel free to submit issues or pull requests.
+
+## 📄 License
+
+[Specify your license here - e.g., MIT, Apache 2.0]
+
+## 📧 Contact
+
+For questions or support, please open an issue in this repository.
+
+---
+
+**Note**: These models were originally part of a larger Android project. The large embedding model (`id_card_embedding_model.keras`) exceeded GitHub's 100MB limit and has been migrated to Hugging Face for easier access and version control.
+---
+license: mit
+---